Scaling using gain factors for use in data detection

ABSTRACT

Data is estimated from a received vector comprising a plurality of communications. A received wireless signal is converted to a baseband signal, which is sampled to produce a received vector. Channel responses are estimated for the received communications. The noise variance is estimated. The noise variance is scaled by a scaling factor. Samples are processed using the estimated channel responses and the scaled noise variance to produce a spread data vector. The spread data vector is despread to recover the data of the received wireless signal.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/408,411, filed Apr. 21, 2006, which is a continuation of U.S. patentapplication Ser. No. 11/175,662, filed Jul. 6, 2005, which issued on May9, 2006 as U.S. Pat. No. 7,042,929, which is a continuation of U.S.patent application Ser. No. 10/327,299, filed Dec. 20, 2002, whichissued on Aug. 9, 2005 as U.S. Pat. No. 6,928,104, which in turn claimspriority from U.S. Provisional Application No. 60/396,823, filed Jul.18, 2002, which are incorporated by reference as if fully set forthherein.

BACKGROUND

This invention generally relates to wireless code division multipleaccess (CDMA) communication systems. In particular, the inventionrelates to scaling using gain factors during data detection in suchsystems.

In wireless CDMA communication systems, multiple communications aretransmitted over a shared spectrum. The individual communications aredistinguished by a code used to transmit each communication. Infrequency division duplex (FDD) CDMA systems, uplink and downlinkcommunications are separated by frequency spectrum. In time divisionduplex (TDD) CDMA or time division synchronous code division multipleaccess (TD-SCDMA) systems, uplink and downlink communications areseparated by time.

One approach to receive communications in such systems is referred to assingle user detection (SUD). In general, SUD is typically performed in atwo part process. First, the received signal is equalized to compensatefor the response of the wireless channel. Second, the equalized signalis despread using the codes of the user or users. SUD is typicallyutilized when all communications experience a same channel response. Thepreferred uses for the SUD is in the downlink and in the uplink when asingle user monopolizes a frequency spectrum (in FDD) or time slot (inTDD or TD-SCDMA). Another approach to receive communications in suchsystems is referred to as multi-user detection (MUD). In MUD, the datafrom all users' data is estimated simultaneously.

In such systems, the individual communications, transmittedsimultaneously, may have varying transmission power levels. Toillustrate, in the uplink, a single User Equipment (UE) may transmitmultiple coded composite transport channels (CCTrCHs). Each CCTrCH mayhave a differing quality of service (QOS), requiring a differenttransmit power level to reach the required QOS. In the downlink, each UEin addition to each individual UE's CCTrCH may require a differingtransmission power level.

Due to the varying power levels between communications, theorthogonality between the received codes is degraded, reducing theperformance of the data detection. Accordingly, it is desirable to havealternate approaches to data detection.

SUMMARY

Data is estimated from a received vector comprising a plurality ofcommunications. A received wireless signal is converted to a basebandsignal, which is sampled to produce a received vector. Channel responsesare estimated for the received communications. The noise variance isestimated. The noise variance is scaled by a scaling factor. Samples areprocessed using the estimated channel responses and the scaled noisevariance to produce a spread data vector. The spread data vector isdespread to recover the data of the received wireless signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified diagram of a transmitter and a receiver usinggain factor scaling for the uplink.

FIG. 1B is a simplified diagram of a transmitter and a receiver usinggain factor scaling for the downlink.

FIG. 1C is a simplified diagram of channel response matrix scaling.

FIG. 2 is a flow chart for applying selective code power scaling.

FIG. 3 is a flow chart for applying total power scaling.

FIG. 4 is a flow chart for iterative selective code power scaling.

FIG. 6A is a simplified block diagram of a multiuser detection receiverusing a scaled noise variance.

FIG. 5B is a simplified block diagram of a multiuser detection receiverusing a scaled channel response matrix.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIGS. 1A, for the uplink, and 1B, for the downlink, are simplifieddiagrams of a transmitter and a receiver using gain factor scaling in aUE and base station. In FIG. 1A at the UE 20, data is spread by amodulation and spreading device 24 using the appropriate codes andmodulated, such as by using quadrature phase shift keying (QPSK), M-aryquadrature amplitude modulation (QAM) or other modulation scheme. Thespread signal is modulated to radio frequency by a modulation device 26and radiated by an antenna 28 or antenna array.

After passing through the wireless air interface 30, an antenna 32 orantenna array at the base station 22 receives the communications. Thereceived communications are converted to a baseband signal, such as by ademodulation device 34. A sampling device 36 samples the basebandsignal, such as at the chip rate or a multiple of the chip rate, toproduce a received vector r. A channel estimation device 38 estimatesthe channel responses for the received communications, as a channelresponse matrix H. All of the H matrixes in the following equations arepresumed to be normalized. The channel estimation is typically performedusing a reference signal, such as the midambles of communication burstsor a pilot code. The channel estimation device 38 also preferablyestimates a variance of the noise σ², although the estimated noisevariance may be derived from other sources. The noise variance σ² isscaled by a scaling device, such as by a factor of 1/P. Preferredderivations for the value of P are explained in detail later. Althoughthe preferred embodiment scales the noise variance σ², the channelresponse matrix H may be scaled by a scaling device 61 instead of thenoise variance, producing H′, as shown in FIG. 1C. One approach toderive H′ is to multiply H by the square root of P, H′=H·√{square rootover (P)}. For such an H′, H′^(H)H′ equals PH^(H)H.

A channel equalizer 42 uses the channel response matrix H and the scalednoise variance σ²/P to produce a spread data vector s. Preferably, thespread data vector s is scaled by a scaling device 64, such as by afactor 1/P Λ^(H), although this scaling device 64 may not be used.(•)^(H) is the conjugate transpose operation. When the scaling device 64is not used, the spread data vector s is passed directly to thedespreader 46. Λ^(H) is a diagonal matrix, which preferred derivationsare explained in detail later. The despreader 46 despreads the scaledspread data or spread data using the codes C of the communications torecover the data d.

In FIG. 1B at the base station 22, data is spread and modulated bymultiple modulation and spreading devices 48 ₁ to 48 _(N) fortransmission to one or multiple UEs 20. The spread signals are combinedby a combiner 50 and modulated to radio frequency by a modulator 52. Themodulated signal is radiated by an antenna 32 or antenna array.

At a UE 20, an antenna 28 or antenna array receives the communicationssent through the wireless air interface 30. The received communicationsare demodulated by a demodulator 54 to baseband. A sampling device 56samples the baseband signal to produce a received vector r. A channelestimation device 58 estimates the channel response matrix H and thenoise variance σ². The noise variance is scaled by a scaling device 60,such as by a factor of 1/P. Alternately, as shown in FIG. 1C, thechannel response matrix H can be scaled. A channel equalizer 62processes the samples using the channel response matrix H and scalednoise variance to produce a spread data vector s. The spread data vectors is scaled by a scaling device 64, such as by a factor 1/P Λ^(H),although scaling device 64 may not be used. When the scaling device 64is not used, the spread data vector s is passed directly to thedespreader 66. The despreader 66 despreads either the scaled or unscaledspread data using the codes C of that UE's communications to recover thedata of the UE's communications. Typically, if some of the receivedcommunications are for other UEs 20, the codes of those communicationsare not used to despread the other UEs' data.

Discussion of preferred algorithms for scaling in data detection areexplained in conjunction with a preferred wideband CDMA (WCDMA) TDDsystem, although the invention is applicable to other systems, such asFDD/WCDMA, TD-SCDMA, CDMA 2000 as well as others.

The received vector r can be modeled per Equation 1.r=AΛd+n  Equation 1

A is the normalized symbol response matrix. Λ is the signal amplitudegain matrix and is a diagonal matrix. n is the noise vector.

When K codes are transmitted at the same time (within the sametimeslot), A=[A₁, . . . , A_(K)] is the normalized symbol responsematrix of the K codes. Λ is preferably of size K·N_(s). N_(s) is thenumber of data symbols in a data field. d=[d₁, . . . , d_(K)] is thedata sequence carried by the K codes. Λ is per Equation 2.$\begin{matrix}{\Lambda = \begin{bmatrix}\Lambda_{1} & \quad & \quad & \quad \\\quad & \Lambda_{2} & \quad & \quad \\\quad & \quad & ⋰ & \quad \\\quad & \quad & \quad & \Lambda_{K}\end{bmatrix}} & {{Equation}\quad 2}\end{matrix}$

Each amplitude gain sub-matrix for a k^(th) code of the K codes, Λ_(k),is a diagonal matrix of size of N_(s) with all the diagonals equal tothe signal amplitudes, g_(k), of the k^(th) code, per Equation 3.$\begin{matrix}{\Lambda_{k} = \begin{bmatrix}g_{k} & \quad & \quad & 0 \\\quad & g_{k} & \quad & \quad \\\quad & \quad & ⋰ & \quad \\0 & \quad & \quad & g_{k}\end{bmatrix}} & {{Equation}\quad 3}\end{matrix}$

To estimate data {circumflex over (d)} in joint detection, a minimummean square error (MMSE) approach per Equation 4 and 5 may be used.{circumflex over (d)}=Λ ⁻¹(A ^(H) A+Λ ⁻²σ² I)⁻¹ A ^(H) r  Equation 4{circumflex over (d)}=Λ ⁻¹ A ^(H)(AA ^(H)+Λ⁻²σ⁻² I)⁻¹ r  Equation 5{tilde over (d)} is the estimated data vector. Equation 4 and 5 areinterchangeable by the matrix inversion lemma.

When all the communications pass through the same propagation channel H,the symbol response matrix A is per Equation 6.A=[A₁, . . . , A_(K)]=[HC₁, . . . , HC_(K)]=H[C₁, . . . ,C_(K)]=HC  Equation 6

H is preferably of size Q·N_(s)+W−1 by Q·N_(s). N_(s) is the number ofdata symbols per data block that the data detection is performed. Q isthe spreading factor of the communications and W is the length of thedelay spread. C is preferably of size Q·N_(s) by K·N_(s).

The received signal for a common channel can be modeled per Equation 7.r=HCΛd+n  Equation 7

Applying a MMSE solution to determine d is per Equation 8.{circumflex over (d)}=Λ ^(H) C ^(H) H ^(H)(HMH ^(H)+σ² I)⁻¹ r  Equation8

The matrix M is the code correlation matrix per Equation 9.M=CGC^(H)  Equation 9

M is preferably of size Q·N_(s). The matrix G is the code power matrix,per Equation 10. $\begin{matrix}{G = {{\Lambda\Lambda}^{H} = \begin{bmatrix}\Lambda_{1}^{2} & \quad & \quad & \quad \\\quad & \Lambda_{2}^{2} & \quad & \quad \\\quad & \quad & ⋰ & \quad \\\quad & \quad & \quad & \Lambda_{K}^{2}\end{bmatrix}}} & {{Equation}\quad 10}\end{matrix}$

The code correlation matrix M is a diagonal dominant matrix with all thediagonals having a same value, such as P. One value for P is the totalpower of all bursts per Equation 11. $\begin{matrix}{P = {\sum\limits_{k = 1}^{K}g_{k}^{2}}} & {{Equation}\quad 11}\end{matrix}$

In general, P is referred to as the code power scale factor. By ignoringthe edge effects of the non-diagonal part of the matrix, anapproximation for M, M, is per Equation 12.{circumflex over (M)}=P·I  Equation 12

By substituting Equation 12 into Equation 8, Equation 13 or 14 results.$\begin{matrix}{\hat{\underset{\_}{d}} = {\frac{1}{P}\Lambda^{H}C^{H}{H^{H}\left( {{HH}^{H} + {\frac{\sigma^{2}}{P}I}} \right)}^{- 1}\underset{\_}{r}}} & {{Equation}\quad 13} \\{\hat{\underset{\_}{d}} = {\frac{1}{P}\Lambda^{H}{C^{H}\left( {{H^{H}H} + {\frac{\sigma^{2}}{P}I}} \right)}^{- 1}H^{H}\underset{\_}{r}}} & {{Equation}\quad 14}\end{matrix}$

Equation 13 and 14 are interchangeable by the matrix inversion lemma.

Equation 14 can be broken down in two stages per Equations 15 and 16.$\begin{matrix}{\hat{\underset{\_}{s}} = {\left( {{H^{H}H} + {\frac{\sigma^{2}}{P}I}} \right)^{- 1}H^{H}\underset{\_}{r}}} & {{Equation}\quad 15} \\{\hat{\underset{\_}{d}} = {\frac{1}{P}\Lambda^{H}C^{H}\hat{\underset{\_}{s}}}} & {{Equation}\quad 16}\end{matrix}$

In Equation 15, the channel equalization is performed. Preferably, thescaling in Equation 15 is performed by scaling devices 40, 60.Preferably, to reduce the complexity in solving equation 15 by thechannel equalizer 42, 62, an approximate Cholesky or fast Fouriertransform based solution is used, although other techniques may be used.

In Equation 16, the despreading is performed. The scaling,${\frac{1}{P}\Lambda^{H}},$during or prior to despreading may or may not be performed. Such scalingtends to improve the decoding performance of turbo-encoded signals.

By careful selection of the code power scaling factor, the overallperformance of the receiver can be improved. The performance of thereceiver can be modeled by Equation 17.ŝ=s+I _(f) +w  Equation 17

I_(f) represents the residual interference after equalization due to theincomplete equalization of channel distortion that results in theinter-symbol interference (ISI) and multiple access interference (MAI).ŝ_(est) of Equation 18 represents the impact of I_(f) on the estimatedspread data. $\begin{matrix}{{\hat{\underset{\_}{s}}}_{est} = {{\hat{\underset{\_}{s}} + {\underset{\_}{I}}_{f}} = {\left( {{H^{H}H} + {\frac{\sigma^{2}}{P}I}} \right)^{- 1}H^{H}H\quad\underset{\_}{s}}}} & {{Equation}\quad 18}\end{matrix}$

w represents the noise after equalization and is per Equation 19.$\begin{matrix}{\underset{\_}{w} = {\left( {{H^{H}H} + {\frac{\sigma^{2}}{P}I}} \right)^{- 1}H^{H}\underset{\_}{n}}} & {{Equation}\quad 19}\end{matrix}$

The interference I_(f) and noise w cannot be reduced simultaneously.Decreasing one tends to increase the other. For a large P, the residualinterference is reduced, but the noise is increased. For a small P, thenoise is reduced but at the cost of increased residual interference.

Two preferred embodiments for power scaling, determining P, are totalpower scaling and selective scaling. In total power scaling, P isdetermined per Equation 20. $\begin{matrix}{P = {P_{T} = {{K \cdot g_{avg}^{2}} = {\overset{M,N}{\sum\limits_{m,{n = 1}}}{K_{m,n}g_{m,n}^{2}}}}}} & {{Equation}\quad 20}\end{matrix}$

M is the number of UEs and N is the number of CCTrCHs per UE. K_(m,n) isthe total number of codes in the n^(th) CCTrCH of the m^(th) UE andg_(m,n) is the gain factor for the n^(th) CCTrCH of the m^(th) UE.

Total power scaling tends to optimize performance over all connectionsequally and not to optimize any connection over the others. Toillustrate, the code power matrix G is per Equation 21. $\begin{matrix}{G = \begin{bmatrix}\Lambda_{1,1}^{2} & \quad & \quad & \quad & \quad & \quad & \quad & \quad \\\quad & ⋰ & \quad & \quad & \quad & \quad & \quad & \quad \\\quad & \quad & \Lambda_{1,N}^{2} & \quad & \quad & \quad & \quad & \quad \\\quad & \quad & \quad & ⋰ & \quad & \quad & \quad & \quad \\\quad & \quad & \quad & \quad & ⋰ & \quad & \quad & \quad \\\quad & \quad & \quad & \quad & \quad & \Lambda_{M,1}^{2} & \quad & \quad \\\quad & \quad & \quad & \quad & \quad & \quad & ⋰ & \quad \\\quad & \quad & \quad & \quad & \quad & \quad & \quad & \Lambda_{M,N}^{2}\end{bmatrix}} & {{Equation}\quad 21}\end{matrix}$

Λ_(m,n) ² is the code power sub-matrix corresponding to the n^(th)CCTrCH of the m^(th) UE. The code power g_(m,n) ² in the matrix G and inG's sub-matrix can be approximated by one single common power in a leastsquare error approach by minimizing Equation 22. $\begin{matrix}{x_{lse} = {\arg\quad{\min\limits_{x}{\sum\limits_{m,n}^{M,N}\quad\left( {x - g_{m,n}^{2}} \right)^{2}}}}} & {{Equation}\quad 22}\end{matrix}$

The solution of least-square-error-power is the average power of allcodes per Equation 23. $\begin{matrix}{g_{avg}^{2} = {\frac{1}{K}{\sum\limits_{{m = 1},{n = 1}}^{M,N}\quad{K_{m,n}g_{m,n}^{2}}}}} & {{Equation}\quad 23}\end{matrix}$

K is the total number of codes transmitted in the time period ofinterest for the system and is per Equation 24. $\begin{matrix}{K = {\sum\limits_{{m = 1},{n = 1}}^{M,N}\quad K_{m,n}}} & {{Equation}\quad 24}\end{matrix}$

The code power scale factor is determined by Equation 25.$\begin{matrix}{P = {{K \cdot g_{avg}^{2}} = {\sum\limits_{m,{n = 1}}^{M,N}\quad{K_{m,n}g_{m,n}^{2}}}}} & {{Equation}\quad 25}\end{matrix}$

g_(avg) ² is the average code power. A measure of the mismatch betweencode powers Δ_(m,n) is per Equation 26.Δ_(m,n) =|g _(m,n) ² −g _(avg) ²|  Equation 26

As illustrated, total power scaling is optimal over all connections byminimizing the code power mismatch over all connections.

In selective code power scaling, P is determined to optimize aparticular UE connection. To optimize a j^(th) UE's connection, Equation27 is used.P=α_(j)P_(T)  Equation 27

α_(j) is a factor for the j^(th) UE that is based on the interferenceand noise level. α_(j) should be adaptively adjusted based on theinterference and noise level for optimum performance of the datadetection. Two preferred equations for deriving α_(j) are per Equations28 and 29. $\begin{matrix}{a_{j} = {\frac{\frac{K}{K_{j}}{\sum\limits_{i = 1}^{I}\quad{K_{i,j}g_{i,j}^{2}}}}{P_{T}} = {\frac{K}{K_{j}}{\sum\limits_{i = 1}^{I}\quad a_{i,j}}}}} & {{Equation}\quad 28} \\{\alpha_{j} = {\frac{\frac{1}{K_{j}}{\sum\limits_{i = 1}^{I}\quad{K_{i,j}g_{i,j}^{2}}}}{P_{T}} = {\frac{1}{K_{j}}{\sum\limits_{i = 1}^{I}\quad\alpha_{i,j}}}}} & {{Equation}\quad 29}\end{matrix}$

K_(j) is the total number of codes carried by that j^(th) UE. K_(i,j) isthe number of codes for the i^(th) CCTrCH of the j^(th) UE. g_(i,j) isthe gain factor for the i^(th) CCTrCH for the j^(th) UE. I is the numberof CCTrCHs of the UE.

Selective code power scaling may also be used to optimize a particularCCTrCH of a UE. To optimize the i^(th) CCTrCH of the j^(th) UE, Equation30 is used.P=α_(i,j)P_(T)  Equation 30

α_(i,j) is a factor for the i^(th) CCTrCH of the j^(th) UE.

Selective code power scaling may also be used to optimize a particularcode of a particular CCTrCH of a UE. To optimize the n^(th) code of thei^(th) CCTrCH of the j^(th) UE, Equation 31 is used.P=α_(n,i,j)P_(T)  Equation 31

α_(n,i,j) is a factor for the n^(th) code of the i^(th) CCTrCH of thej^(th) UE. Two preferred equations for determining α_(n,i,j) areEquations 32 and 33. $\begin{matrix}{a_{n,i,j} = \frac{{Kg}_{i,j}^{2}}{P_{T}}} & {{Equation}\quad 32} \\{a_{n,i,j} = \frac{g_{i,j}^{2}}{P_{T}}} & {{Equation}\quad 33}\end{matrix}$

Two special cases of selective code power scaling are maximum code powerand minimum code power scaling. In maximum code power scaling, themaximum code power is used for the scaling. Maximum code power scalingis most applicable when the over-scaling of code power degrades lessthan the under-scaling of code power. In minimum code power scaling, theminimum code power is used for scaling. Minimum code power scaling ismost applicable when the under-scaling of code power degrades less thanthe over-scaling of code power.

FIG. 2 is one flow chart for applying code power scaling. One UE or UE'sCCTrCH is selected for optimum performance, Step 70. If SUD is beingperformed at the UE the selected CCTrCH is typically with the highestrequired QOS. If SUD is being performed at the base station, the UE ofinterest may be the furthest UE, to reduce that UE's transmission powerlevel. Alternatively, the UE or UE CCTrCH of interest may be the UE orUE CCTrCH requiring the highest quality of service. For the UE/UE CCTrCHof interest, the gain factor(s) for that UE/CCTrCH is determined, Step72. The code scaling factor P is determined from the gain factor(s). SUDis performed using the determined P, Step 74. The code scaling factor Pis used in the data detection, such as by scaling devices 40, 44, 60,64, Step 76. As a result, that UE's/UE CCTrCH's performance isoptimized.

FIG. 3 is a flow chart for total power scaling. Total power scaling ispreferably performed if all the CCTrCHs at a UE or base station requirethe same QOS. Additionally, total power scaling may be applied to reducethe complexity by eliminating the need to optimize the SUD for aparticular UE. The gain factors of all UE codes are determined, Step 78,and the resulting code scaling factor P is determined, Step 80. The SUDis performed using the determined P, Step 82. This approach equallyoptimizes all codes.

In some cases P is not necessarily determined from the gain factors. Forinstance, when a common midamble is used in a downlink allocation, theestimated channel response, H′, has the total power information.Accordingly, the total power is embedded in the estimated channelresponse at the output of the channel estimator, i.e. H′=H·√{square rootover (P)}. In this alternative, gain scaling is not required and steps78 and 80 of FIG. 3 in this situation can be avoided.

FIG. 4 is a flow chart of iterative selective code power scaling. Tooptimize the performances, each UE or UE CCTrCH can be optimized.Sequentially for each UE/UE CCTrCH, the gain factor(s) are determinedand the resulting code scaling factor P is determined, Step 86. Using P,SUD is performed and the data for that UE/UE CCTrCH is estimated, Step88. The process is repeated until each of the UEs/UE CCTrCHs have beenprocessed or all the UEs/UE CCTrCHs of interest have been processed,Step 90. This approach optimizes overall performance for each UE/UECCTrCH at the expense of added complexity and delay.

FIGS. 5A and 5B are simplified block diagrams of gain factor scalingused in a multiuser detection receiver. In multiuser detection, datafrom all codes is detected simultaneously. These approaches to datadetection can also experience improved performance by using gain factorscaling.

In FIG. 5A, the received communications are received by an antenna 100or antenna array. The received communications are demodulated tobaseband by a demodulator 102. The baseband signal is sampled by asampling device 104 to produce a received vector, r. A channelestimation device 106 estimates the channel response as a channelresponse matrix, H. The noise variance σ² is also determined, such as bythe channel estimation device 106 or by another device. The noisevariance σ² is scaled by a scaling device 110, such as by a value 1/P.The channel response matrix H and the noise variance σ² as well as thereceived vector and spreading codes C are input into a multiuserdetection device 108. The multiuser detection device 108 produces a datavector d.

In an alternate approach as shown in FIG. 5B, the channel responsematrix H is scaled. The received communications are received by anantenna 112 or antenna array. The received communications aredemodulated to baseband by a demodulator 114. The baseband signal issampled by a sampling device 116 to produce a received vector, r. Achannel estimation device 118 estimates the channel response as achannel response matrix, H. The noise variance a is also determined,such as by the channel estimation device 118 or by another device. Forsome multiuser detection algorithms, the noise variance a is not used.For those algorithms, the noise variance σ² is not detected. The channelresponse matrix H is scaled by a scaling device 122, such as by a value√{square root over (P)}. The channel response matrix H and the noisevariance σ² as well as the received vector r and spreading codes C areinput into a multiuser detection device 120. The multiuser detectiondevice 120 produces a data vector d. Preferably, for use with multiuserdetectors, P is derived using total power scaling.

Alternately, the gain factor scaling can be performed in conjunctionwith the multiuser detection. For such gain scaling, the multiuserdetection device 108, 120 performs the gain factor scaling.

1. A wireless transmit receive unit (WTRU) comprising: an antennaconfigured to receive a vector comprising a plurality of communications,the communications received in a shared spectrum; a channel estimationdevice configured to estimate a channel response of the received vector,said channel response including a channel response matrix and a noisevariance; and a data estimation device configured to receive a scaledinput, including one of a scaled version of a noise variance or a scaledversion of a channel response, said scaled input scaled by a factorderived from at least one gain factor of the received communications,and to use the channel response and the received vector to detect dataof the plurality of communications.
 2. The WTRU of claim 1 wherein thedata estimation device comprises a multiuser detection device.
 3. TheWTRU of claim 1 wherein the data estimation device comprises a channelequalizer configured to equalize the received vector, based on a channelresponse matrix and a scaled noise variance to produce a spread datavector, and a despreader configured to despread the spread data vectorto recover data of the plurality of communications.
 4. The WTRU of claim3 wherein the equalized received vector is scaled by a scaling factorderived from at least one gain factor.
 5. The WTRU of claim 1 whereinthe at least one gain factor is a gain factor of a communication ofinterest.
 6. The WTRU of claim 1 wherein the scaling factor is derivedfrom gain factors from all of the received communications.
 7. The WTRUof claim 1 wherein the scaling factor is derived from gain factors froma coded composite transport channel of interest.